SentenceTransformers Embedder

The SentenceTransformerEmbedder class is used to embed text data into vectors using the SentenceTransformers library.

​Usage

cookbook/embedders/sentence_transformer_embedder.py

from bitca.agent import AgentKnowledge
from bitca.vectordb.pgvector import PgVector
from bitca.embedder.sentence_transformer import SentenceTransformerEmbedder

embeddings = SentenceTransformerEmbedder().get_embedding("The quick brown fox jumps over the lazy dog.")

# Print the embeddings and their dimensions
print(f"Embeddings: {embeddings[:5]}")
print(f"Dimensions: {len(embeddings)}")

# Example usage:
knowledge_base = AgentKnowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="sentence_transformer_embeddings",
        embedder=SentenceTransformerEmbedder(),
    ),
    num_documents=2,
)

​Params

Parameter	Type	Default	Description
dimensions	int	-	The dimensionality of the generated embeddings
model	str	all-mpnet-base-v2	The name of the SentenceTransformers model to use
sentence_transformer_client	Optional[Client]	-	Optional pre-configured SentenceTransformers client instance